OpenAI is on a spending spree to secure its AI compute supply chain, signing a new deal with AWS as part of its multi-cloud strategy.
The company recently ended its exclusive cloud-computing partnership with Microsoft. It has since allocated a reported $250 billion back to Microsoft, $300 billion to Oracle, and now, $38 billion to Amazon Web Services (AWS) in a new multi-year pact. This $38 billion AWS deal, while the smallest of the three, is part of OpenAI’s diversification plan.
For industry leaders, OpenAI’s actions show that access to high-performance GPUs is no longer an on-demand commodity. It is now a scarce resource requiring massive long-term capital commitment.
The AWS agreement provides OpenAI with access to hundreds of thousands of NVIDIA GPUs, including the new GB200s and GB300s, and the ability to tap tens of millions of CPUs.
This mighty infrastructure is not just for training tomorrow’s models; it’s needed to run the massive inference workloads of today’s ChatGPT. As OpenAI co-founder and CEO Sam Altman stated, “scaling frontier AI requires massive, reliable compute”.
This spending spree is forcing a competitive response from the hyperscalers. While AWS remains the industry’s largest cloud provider, Microsoft and Google have recently posted faster cloud-revenue growth, often by capturing new AI customers. This AWS deal is a plain attempt to secure a cornerstone AI workload and prove its large-scale AI capabilities, which it claims include running clusters of over 500,000 chips.
AWS is not just providing standard servers. It is building a sophisticated, purpose-built architecture for OpenAI, using EC2 UltraServers to link the GPUs for the low-latency networking that large-scale training demands.
“The breadth and immediate availability of optimised compute demonstrates why AWS is uniquely positioned to support OpenAI’s vast AI workloads,” said Matt Garman, CEO of AWS.
But “immediate” is relative. The full capacity from OpenAI’s latest cloud AI deal will not be fully deployed until the end of 2026, with options to expand further into 2027. This timeline offers a dose of realism for any executive planning an AI rollout: the hardware supply chain is complex and operates on multi-year schedules.
What, then, should enterprise leaders take from this?
First, the “build vs. buy” debate for AI infrastructure is all but over. OpenAI is spending hundreds of billions to build on top of rented hardware. Few, if any, other companies can or should follow suit. This pushes the rest of the market firmly toward managed platforms like Amazon Bedrock, Google Vertex AI, or IBM watsonx, where the hyperscalers absorb this infrastructure risk.
Second, the days of single-cloud sourcing for AI workloads may be numbered. OpenAI’s pivot to a multi-provider model is a textbook case of mitigating concentration risk. For a CIO, relying on one vendor for the compute that runs a core business process is becoming a gamble.
Finally, AI budgeting has left the realm of departmental IT and entered the world of corporate capital planning. These are no longer variable operational expenses. Securing AI compute is now a long-term financial commitment, much like building a new factory or data centre.
See also: Qualcomm unveils AI data centre chips to crack the Inference market
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